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- function [varargout] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
- % Optimises the priors of a model (under Laplace approximation)
- % FORMAT [rE,rC] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
- %
- % qE,qC - posterior expectation and covariance of model
- % pE,pC - prior expectation and covariance of model
- % priorfun - inline function that returns priors
- % {rE rC} = priorfun(varargin{:})
- %
- % rE,rC - optimal priors defining a reduced model
- %
- %--------------------------------------------------------------------------
- % This routine optimizes the prior covariance on the free parameters of any
- % model (DCM) under the Laplace approximation. In other words, it assumes
- % that the prior means are fixed and will maximise model evidence with
- % respect to the hyperparameters of a function that returns the prior
- % covariance. This optimization uses the reduced free-energy, based upon
- % the posterior and prior densities of the full model supplied. If the
- % prior covariance function is not specified, this routine will assume a
- % simple diagonal form with a single hyperparameter. In principle, this
- % routine can be used in a flexible and powerful way to emulate
- % hierarchical modeling by using suitable prior covariance functions with
- % unknown hyperparameters. The outputs are the prior moments (mean and
- % covariance) of the optimum model.
- %__________________________________________________________________________
- % Copyright (C) 2008-2011 Wellcome Trust Centre for Neuroimaging
-
- % Karl Friston
- % $Id: spm_dcm_optimise.m 4261 2011-03-24 16:39:42Z karl $
-
- % Compute reduced log-evidence
- %==========================================================================
- % default prior function
- %--------------------------------------------------------------------------
- if nargin == 4
- priorfun = inline('{v2, diag(exp(v1))}','v1','v2');
- varargin{1} = log(diag(pC));
- varargin{2} = pE;
- end
- varargout = spm_argmax('spm_log_evidence',qE,qC,pE,pC,priorfun,varargin{:},6);
- varargin{1} = varargout;
- varargout = priorfun(varargin{:});
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